Learn R Programming

degradr (version 1.0.1)

train_FD001: NASA Turbofan Engine Degradation Simulation Data (FD001)

Description

Run-to-failure simulation data for aircraft turbofan engines generated using C-MAPSS (Commercial Modular Aero-Propulsion System Simulation). This dataset represents engine degradation in the High Pressure Compressor (HPC) module under varying operational conditions.

Usage

data("train_FD001")

Arguments

Format

A data frame with multiple observations (rows) on the following 24 variables (columns):

unit

Engine unit number (identifier)

t

Time in cycles

T2

Total temperature at fan inlet (°R)

T24

Total temperature at LPC outlet (°R)

T30

Total temperature at HPC outlet (°R)

T50

Total temperature at LPT outlet (°R)

P2

Pressure at fan inlet (psia)

P15

Total pressure in bypass-duct (psia)

P30

Total pressure at HPC outlet (psia)

Nf

Physical fan speed (rpm)

Nc

Physical core speed (rpm)

epr

Engine pressure ratio (P50/P2)

Ps30

Static pressure at HPC outlet (psia)

phi

Ratio of fuel flow to Ps30 (pps/psi)

NRf

Corrected fan speed (rpm)

NRc

Corrected core speed (rpm)

BPR

Bypass Ratio

farB

Burner fuel-air ratio

htBleed

Bleed Enthalpy

Nf_dmd

Demanded fan speed (rpm)

PCNfR_dmd

Demanded corrected fan speed (rpm)

W31

HPT coolant bleed (lbm/s)

W32

LPT coolant bleed (lbm/s)

Details

The data was generated for the Prognostics and Health Management (PHM) 2008 data challenge. Each engine unit starts with some initial wear and progresses to failure as efficiency and flow parameters degrade exponentially. The failure criterion is when the health index (calculated from stall margins and EGT) reaches zero. The dataset includes sensor measurements taken at cruise conditions.

References

Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 International Conference on Prognostics and Health Management (pp. 1--9). IEEE. tools:::Rd_expr_doi("10.1109/PHM.2008.4711414")

Examples

Run this code
data(train_FD001)

Run the code above in your browser using DataLab